{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dcf83b17-7109-48c3-8920-2662d498210e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5128068c-2570-42b2-b9b5-8345e8c9641e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cf65785-13f0-44ed-ac0d-d4d3391f5b20",
   "metadata": {},
   "source": [
    "### 设置训练集和测试集数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "283d869f-9975-4056-89e6-716b810f6ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#选择原始图片1 2 3\n",
    "target = 3\n",
    "\n",
    "if target==1:\n",
    "    data_name = '0618'\n",
    "elif target==2:\n",
    "    data_name = '0854'\n",
    "elif target==3:\n",
    "    data_name = '1066'\n",
    "\n",
    "# 设置训练集数据\n",
    "# 获取指定的CSV文件\n",
    "csv_files = glob.glob(f'../RGB_data/data_{data_name}_multi.csv')\n",
    "\n",
    "# 设置测试集数据\n",
    "new_image_path = f'../input_data/{data_name}.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d2b754-0265-4117-a546-538da2f31e9b",
   "metadata": {},
   "source": [
    "# TRAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac41cd2a-913c-4322-aa1b-f187c36f371e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始化一个空的DataFrame来存储合并后的数据\n",
    "data = pd.DataFrame()\n",
    "\n",
    "# 遍历所有CSV文件并将它们合并\n",
    "for file in csv_files:\n",
    "    df = pd.read_csv(file)\n",
    "    data = pd.concat([data, df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c842420-8c91-4fa3-bfa2-1f43a100dabd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读入特征和标签\n",
    "features = data.drop('Label', axis=1) # 按列操作取名为’Label以外的列\n",
    "labels = data['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2bad1adc-8161-43ca-a09d-8f7b5cb4d764",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 归一化\n",
    "scaler = StandardScaler()\n",
    "\n",
    "features = scaler.fit_transform(features)\n",
    "features = pd.DataFrame(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48be8177-c8cf-4f42-bbe5-72cd3071ab0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels,\n",
    "                                     test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de21f354-6867-4729-bc50-a7a23e4ffec0",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 84 candidates, totalling 840 fits\n",
      "Best parameters: {'estimator__C': 100, 'estimator__class_weight': 'balanced', 'estimator__gamma': 0.1, 'estimator__kernel': 'rbf'}\n",
      "Best cross-validation score: 0.8600000000000001\n"
     ]
    }
   ],
   "source": [
    "#网格搜索\n",
    "svc_grid = svm.SVC(kernel='rbf', probability=True)\n",
    "\n",
    "#使用OneVsOneClassifier包装分类器\n",
    "ovo_svc_grid = OneVsOneClassifier(svc_grid)\n",
    "\n",
    "param_grid = {\n",
    "    'estimator__C' : [100, 10, 1, 0.1, 0.01, 0.001, 0.0001],\n",
    "    'estimator__gamma' : [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "    'estimator__kernel' : ['rbf'],\n",
    "    'estimator__class_weight': [None, 'balanced']\n",
    "}\n",
    "\n",
    "#estimator是上面创建的OneVsOneClassifier实例\n",
    "grid_search = GridSearchCV(estimator=ovo_svc_grid, param_grid=param_grid,\n",
    "                           cv=10, scoring='accuracy', verbose=1)\n",
    "#模型训练\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 输出最佳参数和最佳分数\n",
    "print(\"Best parameters:\", grid_search.best_params_)\n",
    "print(\"Best cross-validation score:\", grid_search.best_score_)\n",
    "\n",
    "#使用最佳参数和训练集数据训练最终模型\n",
    "clf = grid_search.best_estimator_\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a8c410a-705f-40af-8443-9d165be3c383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共76个数据, 25个正样本，18个负样本\n",
      "\n",
      "Model accuracy: 0.84\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         5\n",
      "           1       0.75      0.86      0.80         7\n",
      "           2       0.83      0.71      0.77         7\n",
      "\n",
      "    accuracy                           0.84        19\n",
      "   macro avg       0.86      0.86      0.86        19\n",
      "weighted avg       0.85      0.84      0.84        19\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#输出训练结果\n",
    "# 数据量\n",
    "positive_count = data[data['Label'] == 1]['Label'].count()\n",
    "negative_count = data[data['Label'] == 0]['Label'].count()\n",
    "\n",
    "print(f'共{data.shape[0]}个数据, {positive_count}个正样本，{negative_count}个负样本\\n')\n",
    "\n",
    "# 计算并打印准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Model accuracy: {accuracy:.2f}\\n')\n",
    "\n",
    "# 计算并打印分类报告\n",
    "report = classification_report(y_test, y_pred)\n",
    "print('Classification Report:\\n', report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d03120b-0afc-4590-acd2-3ee25fefbac3",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def plot_svm_decision_boundaries(clf, X_train, y_train):\n",
    "    # 创建一个图形窗口，大小为20x10英寸\n",
    "    fig = plt.figure(figsize=(20, 12))\n",
    "\n",
    "    # 创建四个子图，排列为2行2列\n",
    "    # axs是一个2x2的数组，每个元素代表一个子图对象\n",
    "    axs = fig.subplots(2, 2)\n",
    "\n",
    "    # 将子图数组展平，方便索引\n",
    "    axs = axs.flatten()\n",
    "\n",
    "    # BGR (3D) 图\n",
    "    # 创建3D子图，位置在左上角（221表示2行2列中的第1个）\n",
    "    ax1 = fig.add_subplot(221, projection='3d')\n",
    "    # 绘制BGR数据点\n",
    "    ax1.scatter(X_train[0], X_train[1], X_train[2], c=y_train, cmap=plt.cm.Paired)\n",
    "    # 设置坐标轴标签\n",
    "    ax1.set_xlabel('B')\n",
    "    ax1.set_ylabel('G')\n",
    "    ax1.set_zlabel('R')\n",
    "    # 设置子图标题\n",
    "    ax1.set_title('BGR Channels')\n",
    "\n",
    "    # 获取支持向量\n",
    "    support_vectors = clf.support_vectors_\n",
    "    # 在3D图中绘制支持向量\n",
    "    ax1.scatter(support_vectors[:, 0], support_vectors[:, 1], support_vectors[:, 2], s=100,\n",
    "                 linewidth=1, facecolors='none', edgecolors='k')\n",
    "\n",
    "    # 计算并绘制决策边界\n",
    "    w = clf.coef_[0]\n",
    "    b = clf.intercept_[0]\n",
    "    xx = np.linspace(X_train[0].min() - 1, X_train[0].max() + 1, 10)\n",
    "    yy = np.linspace(X_train[1].min() - 1, X_train[1].max() + 1, 10)\n",
    "    X1, X2 = np.meshgrid(xx, yy)\n",
    "    Z = -w[0] / w[2] * X1 - w[1] / w[2] * X2 - b / w[2]\n",
    "    ax1.plot_surface(X1, X2, Z, alpha=0.5, rstride=1, cstride=1, color='k', linewidth=0)\n",
    "\n",
    "    # RG 图 21\n",
    "    ax2 = axs[1]\n",
    "    ax2.scatter(X_train[2], X_train[1], c=y_train, cmap=plt.cm.Paired)\n",
    "    ax2.set_xlabel('R')\n",
    "    ax2.set_ylabel('G')\n",
    "    ax2.set_title('RG Channels')\n",
    "\n",
    "    # 在RG图中绘制支持向量\n",
    "    ax1.scatter(support_vectors[:, 2], support_vectors[:, 1], s=100,\n",
    "                 linewidth=1, facecolors='none', edgecolors='k')\n",
    "\n",
    "    # 绘制RG的决策边界\n",
    "    xx = np.linspace(X_train[2].min() - 1, X_train[2].max() + 1, 500)\n",
    "    yy = np.linspace(X_train[1].min() - 1, X_train[1].max() + 1, 500)\n",
    "    X1, X2 = np.meshgrid(xx, yy)\n",
    "    Z = -w[2] / w[1] * X1 - b / w[1]\n",
    "    ax2.contour(X1, X2, Z, colors='k', levels=[0], linestyles=['-'])\n",
    "\n",
    "    # RB 图 20\n",
    "    ax3 = axs[2]\n",
    "    ax3.scatter(X_train[2], X_train[0], c=y_train, cmap=plt.cm.Paired)\n",
    "    ax3.set_xlabel('R')\n",
    "    ax3.set_ylabel('B')\n",
    "    ax3.set_title('RB Channels')\n",
    "\n",
    "    # 在RB图中绘制支持向量\n",
    "    ax1.scatter(support_vectors[:, 2], support_vectors[:, 0], s=100,\n",
    "                 linewidth=1, facecolors='none', edgecolors='k')\n",
    "\n",
    "    # 绘制RB的决策边界\n",
    "    xx = np.linspace(X_train[2].min() - 1, X_train[2].max() + 1, 500)\n",
    "    yy = np.linspace(X_train[0].min() - 1, X_train[0].max() + 1, 500)\n",
    "    X1, X2 = np.meshgrid(xx, yy)\n",
    "    Z = -w[2] / w[0] * X1 - b / w[0]\n",
    "    ax3.contour(X1, X2, Z, colors='k', levels=[0], linestyles=['-'])\n",
    "\n",
    "    # BG 图 01\n",
    "    ax4 = axs[3]\n",
    "    ax4.scatter(X_train[0], X_train[1], c=y_train, cmap=plt.cm.Paired)\n",
    "    ax4.set_xlabel('B')\n",
    "    ax4.set_ylabel('G')\n",
    "    ax4.set_title('BG Channels')\n",
    "\n",
    "    # 在BG图中绘制支持向量\n",
    "    ax1.scatter(support_vectors[:, 0], support_vectors[:, 1], s=100,\n",
    "                 linewidth=1, facecolors='none', edgecolors='k')    \n",
    "\n",
    "    # 绘制BG的决策边界\n",
    "    xx = np.linspace(X_train[0].min() - 1, X_train[0].max() + 1, 500)\n",
    "    yy = np.linspace(X_train[1].min() - 1, X_train[1].max() + 1, 500)\n",
    "    X1, X2 = np.meshgrid(xx, yy)\n",
    "    Z = -w[0] / w[1] * X1 - b / w[1]\n",
    "    ax4.contour(X1, X2, Z, colors='k', levels=[0], linestyles=['-'])\n",
    "\n",
    "    # 自动调整子图布局，避免重叠\n",
    "    plt.tight_layout()\n",
    "    # 显示图形\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31ca4087-55e9-4956-bfd4-14e5617ee1a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load\n",
    "\n",
    "# 保存模型\n",
    "dump(clf, './model/svm_model_kernel.joblib')\n",
    "\n",
    "# 加载模型\n",
    "clf_loaded = load('./model/svm_model_kernel.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5815d8fd-2c09-4c61-8063-07ba9f8ec997",
   "metadata": {},
   "source": [
    "# TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e893c72-470d-467e-a51d-19b9d31151ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "from joblib import load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a1bf4d3-9411-495c-8c14-b84805b95fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载训练好的模型\n",
    "def load_model(model_path):\n",
    "    return load(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9658fa3c-fff7-4d4f-864f-ca7b95ff9c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "#测试集预处理\n",
    "def preprocess_image(image):\n",
    "    h, w, _ = image.shape\n",
    "    \n",
    "    blue_channel = image[:, :, 0].reshape(-1)\n",
    "    green_channel = image[:, :, 1].reshape(-1)\n",
    "    red_channel = image[:, :, 2].reshape(-1)\n",
    "\n",
    "    img_array = np.stack((blue_channel, green_channel, red_channel), axis=1)\n",
    "\n",
    "    print(img_array.shape)\n",
    "    \n",
    "    return img_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b4da2fb2-4cd8-41b4-92dd-8edd8a99df92",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = './model/svm_model_kernel.joblib'\n",
    "clf = load_model(model_path)\n",
    "\n",
    "#加载测试集图片\n",
    "new_image = cv2.imread(new_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ad350af-aeea-4f98-9c7a-ec25d50bfb83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(125000, 3)\n"
     ]
    }
   ],
   "source": [
    "#将测试集预处理为需要的格式\n",
    "new_features = preprocess_image(new_image)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51d0b2da-7d30-49ec-9278-b1542ad027d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((125000, 3), numpy.ndarray)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用相同的StandardScaler进行标准化\n",
    "new_features = scaler.transform(new_features)\n",
    "new_features.shape, type(new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "43cde813-fa18-4def-8874-85d46abe880f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用模型进行预测\n",
    "prediction = clf.predict(new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c21bf4da-84fd-4e35-8df7-3bed81d7c0a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#预测测试集标签\n",
    "predictions = clf.predict(new_features)\n",
    "\n",
    "#将预测结果应用到图片上\n",
    "#original_image 是原始图片的NumPy数组\n",
    "original_image = new_image\n",
    "height, width, _ = original_image.shape\n",
    "predicted_image = np.zeros_like(original_image)\n",
    "\n",
    "for i in range(height):\n",
    "    for j in range(width):\n",
    "        #获取当前像素的索引\n",
    "        pixel_index = i * width + j\n",
    "        \n",
    "        #根据预测结果设置像素的颜色\n",
    "        if predictions[pixel_index] == 1:\n",
    "            predicted_image[i, j] = (255, 255, 255)  # 白色\n",
    "        else:\n",
    "            predicted_image[i, j] = (0, 0, 0)  # 黑色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "12de4fb7-4df6-4c8b-b408-2273e821ba5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted image saved to ./result_new/pred_1066_OvO.jpg\n"
     ]
    }
   ],
   "source": [
    "# 显示原始图片\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))\n",
    "plt.title('Original Image')\n",
    "plt.axis('off')\n",
    "\n",
    "# 显示预测后的图片\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(cv2.cvtColor(predicted_image, cv2.COLOR_BGR2RGB))\n",
    "plt.title('Predicted Image')\n",
    "plt.axis('off')\n",
    "\n",
    "# 显示图像\n",
    "plt.show()\n",
    "    \n",
    "# 保存预测后的图片\n",
    "new_image_name = os.path.splitext(os.path.basename(new_image_path))[0]\n",
    "save_path = f'./result_new/pred_{new_image_name}_OvO.jpg'\n",
    "cv2.imwrite(save_path, predicted_image)\n",
    "print(f'Predicted image saved to {save_path}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e90dc151-1d7a-4859-bcf0-cf0c92080018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 7.703150510787964 seconds\n"
     ]
    }
   ],
   "source": [
    "end_time = time.time()\n",
    "# 计算并打印运行时间\n",
    "elapsed_time = end_time - start_time\n",
    "print(f\"Elapsed time: {elapsed_time} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8579604a-a5ae-42ca-98b0-abe97374ea7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixel Accuracy (PA): 0.9475\n",
      "Intersection over Union (IoU): 0.7963\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def calculate_performance_metrics(y_true, y_pred):\n",
    "    #计算混淆矩阵\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
    "    \n",
    "    #计算像素准确率（PA）\n",
    "    pa = (tp + tn) / (tp + tn + fp + fn)\n",
    "    \n",
    "    #计算交并比（IoU）\n",
    "    iou = tp / (tp + fp + fn)\n",
    "    \n",
    "    return pa, iou\n",
    "\n",
    "def load_and_mask_binary_image(image_path):\n",
    "    #加载图像\n",
    "    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "    if image is None:\n",
    "        raise ValueError(f\"无法加载图像：{image_path}\")\n",
    "    \n",
    "    #创建掩码以仅保留二值像素（0或255）\n",
    "    mask = (image == 0) | (image == 255)\n",
    "    #应用掩码并转换为二值图像\n",
    "    binary_image = image.copy()\n",
    "    binary_image[~mask] = 0  #将非二值像素设置为0\n",
    "    return binary_image, mask\n",
    "\n",
    "#输入图像的路径\n",
    "test_image_path = save_path  #测试得到的图像路径\n",
    "annotated_image_path = f'../input_data/{data_name}_label.png'  #标注的图像路径\n",
    "\n",
    "#加载并处理图像\n",
    "y_true, true_mask = load_and_mask_binary_image(annotated_image_path)\n",
    "y_pred, _ = load_and_mask_binary_image(test_image_path)\n",
    "\n",
    "#将图像转换为二值数组（0或1）\n",
    "y_true_binary = (y_true > 0).astype(int)\n",
    "y_pred_binary = (y_pred > 0).astype(int)\n",
    "\n",
    "#应用掩码以仅考虑二值像素\n",
    "y_true_binary = y_true_binary[true_mask]\n",
    "y_pred_binary = y_pred_binary[true_mask]\n",
    "\n",
    "#计算性能指标\n",
    "pa, iou = calculate_performance_metrics(y_true_binary, y_pred_binary)\n",
    "\n",
    "print(f\"Pixel Accuracy (PA): {pa:.4f}\")\n",
    "print(f\"Intersection over Union (IoU): {iou:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "051b9da4-412e-41a2-aff8-710b3697220f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
